Steps to be a Professional Data Analysis

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Steps to be a Professional Data Analysis

At the moment, there are many things that will change the course of the Internet world around us. Of these things are big data, Internet things, machine learning and finally artificial intelligence. But what do these things have to do with each other?

In this article, we will show you the road map to study data analysis 

Let's begin by defining what data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names while being used in different business, science, and social science domains. In today's business, data analysis is playing a role in making decisions more scientific and helping the business achieve effective operation.

Data analysis is a proven way for organizations and enterprises to gain the information they need to make better decisions, serve their customers, and increase productivity and revenue. The benefit of data analysis are almost too numerous to count, and some of the most rewarding benefits include getting the right information for your business, getting more value out of IT departments, creating more effective marketing campaigns, gaining a better understanding of customers, and so on.

From the past, we can determine the importance of learning and understanding data analysis. Whether you are in the field of IT or have experience in marketing and business development, following these steps will prepare you to work in the field of data analysis and is an opportunity for those who have the interest to work as a freelancer.

Step 1: Introduction to programming by Python for Data Analysis

Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to effectively work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.

You will start from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames. Along the way, you’ll learn about Python functions and control flow. Plus, you’ll look at the world of data visualizations with Python and create your own stunning visualizations based on real data.

Step 2: Study Statistics and Probability

Statistics and Probability are two related but separate academic disciplines. Statistical analysis often uses probability distributions, and the two topics are often studied together. However, probability theory contains much that is mostly of mathematical interest and not directly relevant to statistics.

Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics too, for example, a scientific, industrial, or social problem, it is conventional, to begin with, a statistical population or a statistical model process to be studied.

Probability is the measure of the likelihood that an event will occur. This is the second part you have to study to build your skills in data analysis journey.

Step 3: Study Computing for Data Analysis

The modern data analysis pipeline involves the collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, to learn how to build these components and connect them using modern tools and techniques.

In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures.

The hands-on component of this course will develop your proficiency with modern analytical tools.

Step 4: Study Machine learning for data science and Analytics

Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.

This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

Step 5: Study Deep Learning

You’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. This is the ideal point-of-entry into the field of AI.

Step 6: Study Relational Data Base

As a data pro, you know that some scenarios—particularly those involving real-time analytics, site personalization, IoT, and mobile apps—are better addressed with NoSQL storage and compute solutions than they are with relational databases.

Step 7: Start Practicing

It is very important to start all the above tools and application to build your application which will be work as your CV in Data Analysis.

All the above courses are available on different platforms and all are free. Only you need to spend time and effort and you can finish it within 3 months.

You have the chance to learn and practice all the above courses  to build your profile as Freelance Data analytics



Hashem Omran

blog_detail.Author: July 28, 2018

Cofounder and CEO at Wide linked Tech.

5 blog_detail.Comments

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    Duppala Sree Harsha

    October 14, 2019

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    September 27, 2019

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    Yuva Rekha

    July 30, 2019

    Thanks for sharing this useful information

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    Yuva Rekha

    July 23, 2019

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    Kiruthi Venkatesh

    May 16, 2019

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